5 Best Python ETL Tools

on ETL • October 20th, 2020 • Write for Hevo

ETL is an essential part of your data stack processes. It lets you activate the data transfer between systems. A good ETL tool single-handedly defines the workflows for your data warehouse. Fortunately, we have you covered, this blog takes you through different Python ETL tools available on the market. Python has been dominating the ETL space for a few years now. There are easily more than a hundred Python tools that act as frameworks, libraries, or software for ETL. In this post, we will be comparing a few of them to help you take your pick. First, let’s look at why you should use Python-based ETL tools.

You will be looking at the following aspects:

Why Python?

Some of the reasons for using Python ETL tools are:

  1. If you want to code your own tool for ETL and are comfortable with programming in Python. 
  2. Your ETL requirements are simple and easily executable.
  3. You have very specific requirements that can only be satisfied by using a custom tool, coded using Python. 

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Top 5 Python ETL Tools

1. Apache Airflow

Apache Airflow is an open source automation tool built on Python used to set up and maintain data pipelines. Technically, Airflow is not an ETL too but rather lets you organize and manage your ETL pipelines using DAGs (Directed Acyclic Graphs). DAGs lets you run a single branch more than once or even skip branches in your sequence when necessary.

A typical Airflow setup will look something like this:

Metadata database > Scheduler > Executor > Workers

The metadata database stores your workflows/tasks, the scheduler, which runs as a service uses DAG definitions to choose tasks and the executor decides which worker executes the task. Workers execute the logic of your workflow/task.

Apache Airflow can seamlessly integrate with your existing ETL toolbox since it’s incredibly useful for management and organization. Apache Airflow make sense when you want to perform long ETL jobs or your ETL has multiple steps, Airflow lets you restart from any point during the ETL process. However, it should be clear that Apache Airflows isn’t a library, so it needs to be deployed and therefore, may not be suitable for small ETL jobs.

One key element of Airflow is that you can easily manage all of DAG workflows via the Airflow WebUI. This means that you can schedule automated workflows without having to manage and maintain them. You will also be able to execute it using a command-line interface

2. Luigi

Luigi is also an opensource Python ETL tool that enables you to develop complex pipelines. It has a number of benefits which includes good visualization tools, failure recovery via checkpoints and a command-line interface.

The main difference between Luigi and Airflow is in the way the dependencies are specified and the tasks are executed. Luigi works with Tasks and Targets. 

Tasks utilize the targets, which are produced by a finished task. So, a task will remove a target, then another task will consume that target and remove another one. This allows the whole process to be straightforward, and workflows to be simple. This is right for simple ETL tasks but not complex tasks.

Luigi is your best choice if you want to automate simple ETL processes like logging. It is important to note that with Luigi you cannot interact with the different processes. Also, Luigi does not automatically sync tasks to workers for you. It does not provide the facility to schedule, alert or monitor as Airflow would. 

3. pandas

Pandas is a Python library that provides you with data structures and analysis tools. It simplifies ETL processes like data cleansing by adding R-style data frames. However, it is time-taking to use as you would have to write your own code. It can be used to write simple scripts easily.

However, when it comes to in-memory and scalability, pandas’ performance may not keep up with expectations.

You should use pandas when you need to rapidly extract data, clean and transform it, and write it to an SQL database/Excel/csv. Once you start working with large data sets, it usually makes more sense to use a more scalable approach.

4. Bonobo

Bonobo is lightweight and easy-to-use. You will be able to deploy pipelines rapidly and parallely. Bonobo can be used to extract data from multiple sources in different formats including CSV, JSON, XML, XLS, SQL, etc. This transformation follows atomic UNIX principles. One of the best qualities about Bonobos is that new users will not have to learn a new API. It is especially easy to use if you have a background in Python. It also has the ability to handle semi-complex schemas. One of the biggest plus points is that it’s open-source and scalable.

Bonobo is suitable when you need simple, lightweight ETL jobs done, and you don’t have the time to learn a new API. Some more key points to note is that Bonobo has an official Docker that lets you run jobs within Docker containers. Moreover, it allows CLI execution as well.

5. petl

petl is an aptly named Python ETL solution. You can extract data from multiple sources and build tables. It is quite similar to pandas in the way it works, although it doesn’t quite provide the same level of analysis. petl is able to handle very complex datasets, leverage system memory and can scale easily too. The best use case for using petl is when you want the basics of ETL without the analytics and the job is not time-sensitive.


In this blog post, you have seen the 5 most popular Python ETL tools available in the market. The tool you choose depends on your business needs, time constraints and budget. The tools we discussed are open source and thus can be easily leveraged for your ETL needs. 

Hevo is a No-code data pipeline. It has pre-built integrations with 100+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial.

Have any further questions? Get in touch with us in the comments section below.

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